Identification and Estimation of Distributional Impacts of Interventions Using Changes in Inequality Measures

By Sergio Firpo. Social Science Research Network, 2016

94 citations recorded by the source corpus.

This paper presents semiparametric estimators of distributional impacts of interventions (treatment) when selection to the program is based on observable characteristics. Distributional impacts of a treatment are calculated as differences in inequality measures of the potential outcomes of receiving and not receiving the treatment. These differences are called “Inequality Treatment Effects” (ITE). The estimation procedure involves a first non-parametric step in which the probability of receiving treatment given covariates, the propensity-score, is estimated. In the second step weighted sample versions of inequality measures are computed using weights based on the estimated propensity-score. Root-N consistency, asymptotic normality, semiparametric efficiency and validity of inference based on the bootstrap are shown for the semiparametric estimators proposed. In addition of being easily implementable and computationally simple, results from a Monte Carlo exercise reveal that its good relative performance in small samples is robust to changes in the distribution of latent selection variables. Finally, as an illustration of the method, we apply the estimator to a real data set collected for the evaluation of a job training program, using several popular inequality measures to capture distributional impacts of the program.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fd42521d233242e68b702e40ad3e8b3c34ce7be5

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Citations in the corpus

Econometric Methods for Program Evaluation (2018, background): “…Dyk, 2004; Cattaneo, 2010; Farrell, 2015; Yang et al., 2016), to quantiles and related treatment effects (Firpo, 2007; Cattaneo, 2010), and to the analysis and interpretation of counterfactual distributional treatment effects (DiNardo et al., 1996; Chernozhukov et al., 2013; Firpo and Pinto, 2016).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “Then we note, following Firpo and Pinto (2016), that the inequality treatment effect (ITE) can be written as τg,ite = υ(FYg )−υ(FY0).”
Recentered influence functions (RIFs) in Stata: RIF regression and RIF decomposition (2020, unknown): “This is similar to Firpo and Pinto (2016). Allows for att, ate and atu.”
Universal Off-Policy Evaluation (2021, background): “Outside RL, works in the econometrics and causal inference literature have also considered quantile treatments [29, 99] and inferences on counterfactual distributions [28, 20, 36], but these methods are not developed for sequential decisions and do not provide any high-confidence bounds with guarant”